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Microarray analysis of genes involve...

Li, Qi.

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Microarray analysis of genes involved in drug addiction.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Microarray analysis of genes involved in drug addiction./
Author:	Li, Qi.
Description:	54 p.
Notes:	Source: Masters Abstracts International, Volume: 49-05, page: 3030.
Contained By:	Masters Abstracts International49-05.
Subject:	Biology, Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1492101
ISBN:	9781124628523

Microarray analysis of genes involved in drug addiction.
Li, Qi.

Microarray analysis of genes involved in drug addiction. - 54 p.

Source: Masters Abstracts International, Volume: 49-05, page: 3030.

Thesis (M.S.)--The University of Texas at San Antonio, 2011.

Drug addiction is a disease of the brain with prominent hazardous effects leading to the collapse of health. It also has vicious influence to the social and economic stability. Long-term administration of drugs will likely lead to addiction, due to the activation and regulation of intracellular pathways in the brain reward system. It is of critical importance to develop a systematic understanding of the molecular mechanisms underlying the development of addiction leading to drug abuse. In this study, we performed secondary analysis of time course microarray expression profiles in response to six addictive drugs (cocaine, ethanol, morphine, methamphetamine, heroin, and nicotine) in the striatum of adult male C57BL/6J inbred mice (Piechota et al, 2010). Several statistical and bioinformatics methods, including two-way ANOVA and hierarchical clustering, were used to identify the genes that showed differential expression with the drug treatments over the time course. Gene ontology and pathway analysis were used to classify the functional categories of the drug-responsive genes and elucidate the cellular networks involving these genes or gene products.

ISBN: 9781124628523Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Microarray analysis of genes involved in drug addiction.
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020 $a 9781124628523
035 $a (UMI)AAI1492101
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040 $a UMI $c UMI
100 1 $a Li, Qi. $3 1269967
245 1 0 $a Microarray analysis of genes involved in drug addiction.
300 $a 54 p.
500 $a Source: Masters Abstracts International, Volume: 49-05, page: 3030.
500 $a Adviser: Yufeng Wang.
502 $a Thesis (M.S.)--The University of Texas at San Antonio, 2011.
520 $a Drug addiction is a disease of the brain with prominent hazardous effects leading to the collapse of health. It also has vicious influence to the social and economic stability. Long-term administration of drugs will likely lead to addiction, due to the activation and regulation of intracellular pathways in the brain reward system. It is of critical importance to develop a systematic understanding of the molecular mechanisms underlying the development of addiction leading to drug abuse. In this study, we performed secondary analysis of time course microarray expression profiles in response to six addictive drugs (cocaine, ethanol, morphine, methamphetamine, heroin, and nicotine) in the striatum of adult male C57BL/6J inbred mice (Piechota et al, 2010). Several statistical and bioinformatics methods, including two-way ANOVA and hierarchical clustering, were used to identify the genes that showed differential expression with the drug treatments over the time course. Gene ontology and pathway analysis were used to classify the functional categories of the drug-responsive genes and elucidate the cellular networks involving these genes or gene products.
590 $a School code: 1283.
650 4 $a Biology, Bioinformatics. $3 1018415
690 $a 0715
710 2 $a The University of Texas at San Antonio. $b Biology. $3 1676088
773 0 $t Masters Abstracts International $g 49-05.
790 1 0 $a Wang, Yufeng, $e advisor
790 1 0 $a Wang, Yufeng $e committee member
790 1 0 $a Cassill, J. Aaron $e committee member
790 1 0 $a Livi, Carolina B. $e committee member
790 $a 1283
791 $a M.S.
792 $a 2011
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1492101